We can apply this to solve enterprise data challenges and answer enterprise domain specific questions. When we applied these MRC algorithms to the book, Future Computed by Brad Smith and Harry Shum, it was incredible to see that we can answer so many interesting questions. R-NET applies a self-matching attention mechanism to refine the representation by matching the passage against itself, which effectively encodes information from the whole passage. Microsoft researchers today have been able to surpass human-level parity on SQuAD dataset using an unique MRC algorithm called R-NET: Machine reading comprehension with self-matching networks. Last year, China’s Alibaba outperformed humans when tested by the. MRC makes it possible for systems to read, infer meaning and immediately deliver answers while sifting through enormous data sets. researchers, machine reading comprehension (MRC) has been a challenging goal, but an important one. With a question in mind, ReasoNet reads a document repeatedly, each time focusing on different parts of the document until a satisfying answer is found or formed. Machine Reading Comprehension (MRC) For A.I. Using a novel neural network architecture called the Reasoning Network (ReasoNet), researchers were able to mimic the inference process of human readers. MRC requires modeling complex interactions between the context and the query.
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